Jev for Home Assistant

Ask TypeSafe Jev questions about your house and get numbers back. Jev is a decision model rather than a chat model, so it answers a typed question with a probability, a choice or a score, and this integration turns each answer into an entity you can automate on.

Not affiliated with TypeSafe. The API client is jevclient.

What it does

• Questions in configuration.yaml become sensors: a probability, one of your options with its distribution, or a number that can land between levels.
• Four actions answer inside an automation and return a response variable: jev.noul, jev.choice, jev.score and jev.ask.
• Point a question at entities, devices, areas, floors or labels in the normal picker and the state is built for you, so no template is needed.
• Reports what it spends: calls, input tokens and estimated cost per day, plus a daily token budget that halts evaluation when it trips.
• Thirteen worked examples, four of them pairing Jev with an LLM.

automation:
  - alias: Remind about the washing
    triggers:
      - trigger: state
        entity_id: binary_sensor.jev_laundry_forgotten
        to: "on"
        for: "00:10:00"
    actions:
      - action: notify.mobile_app
        data:
          message: The washing is done and still in the machine.

Installation

Requires Home Assistant 2026.9 or newer and an API key from typesafe.ai.

HACS

Not in the HACS default list yet, so add it as a custom repository once. hacs/default#11052 is queued; when it merges, steps 1 and 2 go away.

1. HACS, then the three dot menu, then Custom repositories.
2. Paste https://github.com/AboveColin/HA-Jev, set Type to Integration, Add.
3. Search HACS for Jev, then Download.
4. Restart Home Assistant.

Manual

Copy custom_components/jev from the latest release into your config/custom_components/ directory and restart. HACS will not update a copy installed this way.

Configuration

Settings, Devices and services, Add integration, then Jev (TypeSafe). The API key is the only thing it asks for, and it is checked before the entry is created.

Use Reconfigure to replace the key later, which keeps your entities and history.

Actions

- action: jev.noul
  response_variable: laundry
  target:
    entity_id: sensor.washing_machine_power
  data:
    instructions: Is the laundry finished but still sitting in the machine?
    background: >-
      This machine draws under 5 W when idle and over 300 W while a programme runs.
    threshold: 0.7
- if: "{{ laundry.is_true }}"
  then:
    - action: notify.mobile_app
      data: { message: The washing is done and still in the machine. }

All four take a template in state, or an object, or a list. They also take background: for standing facts about how to read the state, which is worth more attached to the question than to the state.

Sensors

jev:
  - name: Laundry
    scan_interval: 300
    entities:
      - sensor.washing_machine_power
      - binary_sensor.laundry_door
    questions:
      - name: Laundry forgotten
        type: noul
        instructions: Is the laundry finished but still sitting in the machine?
        background: >-
          This machine draws under 5 W when idle and over 300 W while a programme runs.
        threshold: 0.7
      - name: Nudge urgency
        type: score
        instructions: How urgently should someone be reminded?
        criteria: [Not at all, When convenient, Right now]

A context is one request, so keep related questions together. It is evaluated on scan_interval, or when an entity it watches changes, debounced by 5 seconds. Adding threshold: to a noul also creates a binary sensor to trigger on.

Examples

The LLM examples use ai_task.generate_data, so they work with Google Generative AI, OpenAI, Anthropic or a local Ollama. The voice command router follows TypeSafe's own smart home demo and builds its device options from your entity registry, so the answer is an entity_id you can act on.

Measurements

docs/measurements.md has what was measured against the live API: what an entity costs in tokens, why batching is nearly free, real latency from Europe against the published figure, and the two findings that changed this code.

Known limitations

• Answers carry no reasoning, so there is nothing to audit afterwards.
• Confidence has no published calibration evidence. Treat 0.9 as higher than 0.6 until you have measured it on your own questions.
• Slower from Europe than the published 70 to 500 ms. Fine for a doorbell, too slow for a tight loop.
• Not for safety decisions. A probability with no explanation should not hold a lock, a heater or a smoke alarm.

Troubleshooting

Turn on debug logging first. It prints every state sent, which is usually the answer:

logger:
  logs:
    custom_components.jev: debug

Contributing

Issues and pull requests welcome.

pip install -r requirements-test.txt
pytest

103 tests run the integration inside a real Home Assistant with the API client replaced, so the suite spends nothing. quality_scale.yaml tracks this against Home Assistant's quality scale, and mypy --strict runs in CI.

Changelog

See the release history.